Differential Diagnosis of Pulmonary Diseases using Convolutional Neural Network with LSTM Architecture

Differential Diagnosis of Pulmonary Diseases using Convolutional Neural Network with LSTM Architecture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differential Diagnosis of Pulmonary Diseases using Convolutional Neural Network with LSTM Architecture B. H. Shekar, Shazia Mannan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4737344/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pulmonary disorders, including conditions such as Pneumonia, Tuberculosis, and COVID-19, affect millions worldwide, presenting a major global health challenge. Accurate and timely detection of these diseases is crucial for effective treatment and patient care. This research paper introduces a novel CNN-LSTM model for effectively differentiating between and categorizing multiple lung disorders. Leveraging a dataset comprising 4529 chest X-rays (CXRs) collected from Shenzhen, Montgomery, Belarus, and COVID-19 Radiography datasets, the study aims to enhance disease diagnosis accuracy. Contrast-limited Adaptive Histogram Equalization and median filtering techniques have been applied to enhance image quality and reduce noise. Although LSTM networks are typically used for sequential data, this study employed them to capture spatial dependencies within 2D medical images. This approach allowed the model to learn complex representations and retain context from different image regions, enhancing feature extraction and overall performance. LSTM worked by exploiting the neighborhood relationships between pixels in CXRs by treating image patches as sequences. This approach enabled the model to better understand and utilize spatial information, thereby improving accuracy. Furthermore, baseline CNN and CNN-Bi LSTM models were also executed for a comparative study. Among the implemented models, CNN-LSTM emerged as the most effective, achieving an accuracy of 96.26% and precision, recall, F1 score of 96.44%, 96.62%, and 96.49%, respectively. It was followed by CNN-BiLSTM and CNN models with accuracy rates of 95.58% and 94.24%. pulmonary Disease Classification CNN CNN-LSTM CNN-Bi LSTM. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4737344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330682452,"identity":"d2b9bf97-7fc2-4187-92e0-fce6fe71c40d","order_by":0,"name":"B. H. 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Accurate and timely detection of these diseases is crucial for effective treatment and patient care. This research paper introduces a novel CNN-LSTM model for effectively differentiating between and categorizing multiple lung disorders. Leveraging a dataset comprising 4529 chest X-rays (CXRs) collected from Shenzhen, Montgomery, Belarus, and COVID-19 Radiography datasets, the study aims to enhance disease diagnosis accuracy. Contrast-limited Adaptive Histogram Equalization and median filtering techniques have been applied to enhance image quality and reduce noise. Although LSTM networks are typically used for sequential data, this study employed them to capture spatial dependencies within 2D medical images. This approach allowed the model to learn complex representations and retain context from different image regions, enhancing feature extraction and overall performance. 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